Definition
Attribute mapping is the process of connecting source data fields to the corresponding fields in a target schema, so incoming supplier or system data lands in the right, standardized attributes. It answers a simple question: which catalog attribute does this source column mean?
Key points
- It aligns source fields to target schema fields.
- It is essential when many sources feed one catalog.
- It precedes normalization — first get data to the right field, then standardize the values.
- Mappings can be maintained as reusable templates per supplier or source system.
How does attribute mapping work in practice?
For each source, teams build a mapping that says "supplier field X → catalog attribute Y." Well-run operations store these as reusable templates so onboarding a familiar source is fast, and flag unmapped fields for review so data is never silently dropped.
Common pitfalls
- One-off manual mapping for every file instead of reusable templates.
- Silently dropping unmapped source fields.
- Mapping to inconsistent or duplicate target attributes.
FAQ
Why is attribute mapping important?
Every source names and structures its data differently. Without mapping, incoming data lands in the wrong fields or gets dropped, breaking search, filters, and completeness. Mapping is what lets many sources feed one consistent catalog.
What is the difference between mapping and normalization?
Mapping decides which target field a source field belongs to. Normalization then standardizes the values inside that field. Mapping gets the data to the right place; normalization makes the values consistent once it arrives.